How Machine Learning Can Help with Urban Development
An experimentation project has demonstrated the capabilities of machine learning in urban development. It used images as a starting point and came up with interesting and useful applications.
“I read data science papers on how machine vision algorithms can be used with satellite imagery. I immediately saw a connection to what we had been doing,” Antti Kauppi, architect at Arkkitehdit Sankari, explains. “Most people associate image recognition with Google’s visual searches. Google can distinguish whether a photo shows a cat or another animal, for example. We went a step further.”
An Experiment with Open Urban Imagery
Arkkitehdit Sankari Oy, a Finnish architectural design firm began the experimentation project CityCNN in May 2018. It received funding from KIRA-digi, the Finnish government’s digitalization program for the built environment. CityCNN explored the possibilities of using machine learning and open data for urban development.
Kauppi collected data from Espoo, Finland’s second-largest city on the outskirts of Helsinki. He created a piece of Python software to retrieve data from the city’s server.
Kauppi has done programming for many years but does not consider himself an AI expert: “My special skill is to apply the technology to our business. It’s much like using Photoshop. You can use it successfully even if you don’t master its inner workings.”
Using Competing Neural Networks
The experiment used so-called generative adversarial networks (GANs). It is a machine learning technique in which you match two neural networks against each other.
The first network, the generator, creates new images. The second network, the discriminator, uses real images and takes in the newly generated images. It evaluates whether an image is real or generated. In repeating this process over and over again, the generator and the discriminator become more accurate and, as a result, the generated images improve.
Conditional adversarial networks used in CityCNN are an extension of the basic technique. They are trained with image pairs. They can create photographic images from line drawings or convert an impressionist painting into a photograph, for example.
“We use Amazon’s cloud for computational power. I’ve trained the network with hundreds of image pairs,” Kauppi explains. He shows examples of aerial photos that the network has converted into city plans. By reversing the neural networks, the system created aerial illustrations using city plans.
Another CityCNN application marked buildings in a satellite image with a color. Kauppi likens it to a five-year-old who’s given crayons and told to color all the buildings. Reversing the action, the neural network can create satellite images from building masses, automaticallly adding roads and streets and even parking lots for larger buildings. It has learned what the landscape in Espoo looks like and mimics it fairly accurately.
Kauppi gives a live demonstration of the network’s capability. He draws rectangles in an empty window and the machine creates a counterpart in another. When he draws a small rectangle in the middle, the machine treats it as a house in the middle of a tree-covered area. A long narrow line ends up being a street with houses on both sides. When Kauppi draws large building masses, the network sees them as an industrial complex or warehouse and creates adjoining large parking areas automatically.
All the visualizations are algorithmic and based on mathematics. The machine does not “understand” the context. However, its behavior looks disturbingly human.
An application that may have a practical use right away is a tool that identifies areas for potential supplementary development. Kauppi had taken aerial photos of Espoo’s residential areas and used a paint program to mark spaces he deemed suitable for infill. Using 500 image pairs as training material, he taught the network to do the same to any satellite image. This way, the machine could quickly spot all the potential areas for supplementary development. Intelligently, it did not flag parks or woods.
“If we gave 5,000 image pairs to experts and had them mark meaningful things on the images, the network would learn how to do the same on a national level,” Kauppi envisions. “That sounds like a lot of work, but it would take about 10 days and a few thousand euros for the computing, which is reasonable. After the initial training, the network can generate new images in milliseconds.”
If you combine image data with other types of urban data, new applications emerge. Kauppi mentions Mapita’s Maptionnaire app, which allows citizens to give locational feedback on their urban experiences. If people tag certain areas as being unsafe or pleasant, a machine learning algorithm could automatically locate other similar places to help with city plans of the future.
“Now that we’ve completed this experiment satisfactorily, we’ll report the results and share our experiences openly,” says Kauppi. “We’re happy to discuss how to develop these ideas further.”
You can email Antti Kauppi at .